The negative effect of financial crimes on financial institutions has grown dramatically over
the years. To detect crimes such as credit card fraud, several single and hybrid machine …

The paper contributes to a growing body of empirical work on regulatory technology by
proposing machine learning models to detect fraud in financial markets. The recent spate of …

Transparency International estimates that the costs of corruption in public procurement
reach between 20 and 25% of the contract value, sometimes reaching 40–50%. In this study …

Increasingly, extracting knowledge from data has become an important task in organizations
for performance improvements. To accomplish this task, data-driven Prognostics and Health …

In today's big-data era, enterprises are able to generate complex and non-structured
information that could cause considerable challenges for CPA firms in data analysis and to …

Open Innovation (OI) models have been studied in many fields. However, the challenges
and opportunities of a possible OI paradigm application in external auditing have been …

Neuronets trained by a weights-and-structure-determination (WASD) algorithm are known to
resolve the shortcomings of traditional back-propagation neuronets such as slow training …

This paper is a case study of utilizing machine learning for developing a decision-making
system for auditors before initializing the audit fieldwork of public firms. Annual data of 777 …

The aim of the research is to find the best performance both of logistic regression and linear
discriminant which their threshold uses some various values. The performance tools used …

Investment fraud continues to be a severe problem in the Canadian securities industry. This
paper aims to employ machine learning algorithms and artificial neural networks (ANN) to …